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Vergaray Ormeño, C.E

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Authors

Vergaray Ormeño, C.E

ten Caten, A

Alves Henriques, J.P

Maciel Reva, M.A

Sousa Silva, M

Vergaray Ormeño, C.E

ten Caten, A

Alves Henriques, J.P

Maciel Reva, M.A

Sousa Silva, M

ten Caten, A

Vergaray Ormeño, C.E

Henriques, J.A

Reva, M.M

Silva, M.S

Topics

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Year

2026

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Authors

Filter results3 paper(s) found.
1. Influence of Spectral Pre-processing and Signal-to-noise Ratio on Soil Fertility Prediction Models Soil and crop sensing through Vis–NIR spectroscopy is key to expanding the spatial and temporal coverage of precision agriculture initiatives. In this scenario spectral preprocessing and signal-to-noise ratio (SNR) significantly influence the accuracy and stability of soil fertility predictions based on spectroscopy. However, their impact is often underestimated, despite their effect on spectral quality and model performance. This study evaluated the influence of different spectral preprocessing... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva
2. Geostatistical Comparison of Soil Fertility Maps Derived from Laboratory Soil Analyses and Spectral Model Predictions Spatial mapping of soil fertility attributes is a key tool for precision agriculture and efficient management of agricultural fields. Soil spectroscopy is lately being presented as an efficient alternative to soil wet chemistry analysis; however, the spatial reliability of spectrally predicted data must be carefully evaluated. In this study, spatially interpolated maps generated from observed laboratory measurements and spectral predictions, of three soil attributes related to primary soil fertility... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva
3. Recalibration of Spectral Models Using Spiking Techniques for Predicting Primary Nutrient Attributes Soil spectral libraries are an important strategy for rapid prediction of soil fertility atributes in digital agriculture projects. However, their predictive performance may decline when models are applied outside the specific conditions for which they were calibrated. Even in regions with similar pedoclimatic characteristics, management practices can limit model accuracy. In this context, spiking-based recalibration has been proposed as a practical strategy to improve model performance, although... A. Ten Caten, V. Ormeño, J.A. Henriques, M.M. Reva, M.S. Silva

Filter results3 paper(s) found.

1. Influence of Spectral Pre-processing and Signal-to-noise Ratio on Soil Fertility Prediction Models

Soil and crop sensing through Vis–NIR spectroscopy is key to expanding the spatial and temporal coverage of precision agriculture initiatives. In this scenario spectral preprocessing and signal-to-noise ratio (SNR) significantly influence the accuracy and stability of soil fertility predictions based on spectroscopy. However, their impact is often underestimated, despite their effect on spectral quality and model performance. This study evaluated the influence of different spectral preprocessing... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva

2. Geostatistical Comparison of Soil Fertility Maps Derived from Laboratory Soil Analyses and Spectral Model Predictions

Spatial mapping of soil fertility attributes is a key tool for precision agriculture and efficient management of agricultural fields. Soil spectroscopy is lately being presented as an efficient alternative to soil wet chemistry analysis; however, the spatial reliability of spectrally predicted data must be carefully evaluated. In this study, spatially interpolated maps generated from observed laboratory measurements and spectral predictions, of three soil attributes related to primary soil fertility... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva

3. Recalibration of Spectral Models Using Spiking Techniques for Predicting Primary Nutrient Attributes

Soil spectral libraries are an important strategy for rapid prediction of soil fertility atributes in digital agriculture projects. However, their predictive performance may decline when models are applied outside the specific conditions for which they were calibrated. Even in regions with similar pedoclimatic characteristics, management practices can limit model accuracy. In this context, spiking-based recalibration has been proposed as a practical strategy to improve model performance, although... A. Ten Caten, V. Ormeño, J.A. Henriques, M.M. Reva, M.S. Silva